Machine Learning System Design: Ad click prediction

<p>Develop a machine learning model to forecast the likelihood of an ad receiving clicks.</p> <p><img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*6H9AgW91pIoxGPFS7yX4oQ.png" style="height:255px; width:700px" /></p> <p>Before progressing, it&rsquo;s crucial to grasp the nuances of the ad serving backdrop. Initially, the ad request traverses a waterfall model, wherein publishers endeavor to dispose of their inventory through direct sales characterized by high CPM (Cost Per Million). In instances where this approach does not materialize, the publishers then circulate the impression across various networks until a sale is secured.</p> <h1>Metrics design and requirements</h1> <h2>Metrics</h2> <p>In the training phase, it&rsquo;s advisable to concentrate on machine learning metrics, sidestepping revenue or CTR (Click Through Rate) metrics momentarily. Here are two fundamental metrics to consider:</p> <p><a href="https://medium.com/@jh.baek.sd/day-11-machine-learning-system-design-ad-click-prediction-a0cf05ab88a5">Website</a></p>